Journal of System Simulation
Abstract
Abstract: Establishing an accurate mathematical model of main steam temperature is the basis of improving the performance of control system. Aiming at the problems of early maturity and slow convergence in traditional particle swarm optimization (PSO) algorithm in model identification, an improved PSO algorithm with shrinkage factor is proposed. The algorithm improves the global optimization capability and convergence speed of the algorithm by adjusting the shrinkage factor. The on-site operating data of a 350 MW circulating fluidized bed (CFB) boiler in a power plant in Shanxi province are used in the identification of the main steam model parameters, and the improved PSO algorithm is used to optimize the model parameters of the main steam temperature system. The validity of the model is verified by actual data on-site, which lays the foundation for the optimization of main steam temperature control of CFB boilers.
Recommended Citation
Cao, Zhenqian; Jiang, Yin; and Zhang, Jinhua
(2021)
"Identification of Main Steam Temperature System Based on Improved Particle Swarm Optimization,"
Journal of System Simulation: Vol. 33:
Iss.
10, Article 14.
DOI: 10.16182/j.issn1004731x.joss.20-0609
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss10/14
First Page
2411
Revised Date
2020-09-09
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0609
Last Page
2419
CLC
TP391.9
Recommended Citation
Cao Zhenqian, Yin Jiang, Zhang Jinhua. Identification of Main Steam Temperature System Based on Improved Particle Swarm Optimization[J]. Journal of System Simulation, 2021, 33(10): 2411-2419.
DOI
10.16182/j.issn1004731x.joss.20-0609
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